American Institute of Certified Planners (AICP) Practice Exam

Selecting a statistical sample from a large population allows researchers to estimate the characteristics of that population without having to assess every single member. One of the key strengths of sampling is that it enables the calculation of the margin of error and confidence intervals, which provide a mathematical estimate of the accuracy of the results. This is crucial in reflecting how close the sample estimates are likely to be to the true population parameters.

When a sample is drawn, statistical methods can be applied that quantify the uncertainty in the estimates derived from that sample. For instance, using the sample mean or proportion, one can generate confidence intervals that indicate the range within which the true population mean or proportion is expected to fall, given a certain level of confidence. This aspect of sampling is foundational in fields such as social science research, market research, and policy analysis, as it equips planners and analysts with the necessary information to make informed decisions based on a subset rather than labor-intensive full population assessments.

The other options, while related to sampling techniques, do not fundamentally encapsulate the inherent capability of statistical sampling to provide accuracy estimates. For instance, reducing effort is a benefit but does not reflect the hallmark of sampling accuracy estimation. Similarly, unbiased sampling is an ideal rather than a guarantee, and the adequacy of